Title: "A Journey to Greener Choices - Building an AI-Powered Eco-Advisor"
Tagline: "Empowering Your Journey to a Greener Future"
Project Inspiration:
Our inspiration for this project was to create a tool that empowers individuals to make more eco-conscious choices, specifically in the realm of vehicle selection. With growing concerns about climate change and environmental sustainability, we wanted to build a solution that guides users toward vehicles with lower greenhouse gas emissions and improved fuel efficiency.
What We Learned:
Throughout the project, we learned about the significance of harnessing AI and machine learning to tackle real-world environmental challenges. We acquired a deep understanding of data preprocessing, building and training artificial neural networks (ANNs), and creating content-based recommendation systems.
We also realized that collaboration and interdisciplinary knowledge are crucial when working on projects that aim to make a positive impact. We combined expertise in data science, machine learning, and web development to create an integrated solution.
Building the Project:
Data Collection and Preprocessing: We collected vehicle data, including various attributes such as engine size, cylinders, fuel type, and greenhouse gas emissions. We cleaned and standardized the data to prepare it for the ANN model and content-based recommender.
Artificial Neural Network (ANN): We constructed an ANN to predict vehicle emissions (kg CO2e). The model was trained on a dataset of vehicle attributes and actual emissions values.
Content-Based Recommender: We developed a content-based recommender system that leveraged the ANN's predictions to suggest vehicles with low emissions based on user preferences. We employed cosine similarity to identify similar vehicles.
Web Application: To make the solution accessible to a wider audience, we built a web application using Python and popular frameworks like Flask. Users can interact with the AI-powered eco-advisor through a user-friendly interface.
Challenges Faced:
Data Quality: Ensuring the dataset was clean, complete, and representative was challenging. Missing values and outliers needed to be addressed.
Model Training: Training an accurate ANN model required significant experimentation with hyperparameters, architectures, and optimization techniques.
User Interface: Designing a user-friendly web application interface was a challenge. We wanted users to easily input their preferences and receive recommendations.
In summary, our project reflects a journey to leverage advanced technologies to create a positive impact on the environment. We aimed to inspire greener choices, provide eco-friendly vehicle recommendations, and learned that interdisciplinary collaboration is key to addressing complex environmental challenges.
Built With
- jupyter
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